lllyasviel/stable-diffusion-webui-forge
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lllyasviel
2024-01-29 08:47:50 -08:00
parent b0684335b8
commit dcdc80810e
21 changed files with 57 additions and 51 deletions
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25
extensions-builtin/sd_forge_controlnet_example/scripts/sd_forge_controlnet_example.py
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@@ -7,7 +7,7 @@ import numpy as np
from modules import scripts
from modules.shared_cmd_options import cmd_opts
from modules.paths import models_path
from modules_forge.shared import shared_preprocessors
from modules.modelloader import load_file_from_url
from ldm_patched.modules.controlnet import load_controlnet
from modules_forge.controlnet import apply_controlnet_advanced
@@ -75,24 +75,23 @@ class ControlNetExampleForge(scripts.Script):
width = W * 8
batch_size = p.batch_size
input_image = cv2.resize(input_image, (width, height))
canny_image = cv2.cvtColor(cv2.Canny(input_image, 100, 200), cv2.COLOR_GRAY2RGB)
preprocessor = shared_preprocessors['canny']
from modules_forge.shared import shared_preprocessors
a = 0
# detect control at certain resolution
control_image = preprocessor(
input_image, resolution=512, slider_1=100, slider_2=200, slider_3=None)
# # Or you can get a list of preprocessors in this way
# from modules_forge.shared import shared_preprocessors
# canny_preprocessor = shared_preprocessors['canny']
# canny_image = canny_preprocessor(
# input_image, resolution=512, slider_1=100, slider_2=200, slider_3=None)
# here we just use nearest neighbour to align input shape.
# You may want crop and resize, or crop and fill, or others.
control_image = cv2.resize(
control_image, (width, height), interpolation=cv2.INTER_NEAREST)
# Output preprocessor result. Now called every sampling. Cache in your own way.
p.extra_result_images.append(canny_image)
p.extra_result_images.append(control_image)
print('Preprocessor Canny finished.')
control_image = numpy_to_pytorch(canny_image)
control_image_bchw = numpy_to_pytorch(control_image).movedim(-1, 1)
unet = p.sd_model.forge_objects.unet
@@ -131,7 +130,7 @@ class ControlNetExampleForge(scripts.Script):
advanced_frame_weighting = None
advanced_sigma_weighting = None
unet = apply_controlnet_advanced(unet=unet, controlnet=self.model, image_bchw=control_image.movedim(-1, 1),
unet = apply_controlnet_advanced(unet=unet, controlnet=self.model, image_bchw=control_image_bchw,
strength=0.6, start_percent=0.0, end_percent=0.8,
positive_advanced_weighting=positive_advanced_weighting,
negative_advanced_weighting=negative_advanced_weighting,